We view automation of knowledge base refinement as improvements to a domain theory. Techniques are described that we have developed to handle three types of domain theory pathologies: incorrectness, inconsistency, and incompleteness. The major sources of power of our learning method are a confirmation theory that connects the domain theory to underlying domain theories, the use of an explicit representation of the strategy knowledge for a generic problem class (e.g., heuristic classification)

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... at is separate from the domain theory (e+, medicine) to be improved, and lastly an explicit, modular, and declarative knowledge representation for the domain theory. 1986). These shells use a problem-solving method called heuristic classification, which is the process of selecting a solution out of a pre-enumerated solution set, using heuristic techniques (Clancey, 1985). The Knowledge Base Refinement as Improving an Incorrect Domain Theory